Process mining for individualized behavior modeling using wireless tracking in nursing homes

Appendicectomy pathway: Insights from electronic medical records of a local health district in Australia

BACKGROUND

This study aims to identify the common pathways of appendicectomy, the most common emergency surgery in Australia's public hospitals and any variations within a regional public health district in New South Wales, Australia.

METHODS

We analyzed the electronic medical records of 3,943 patients who underwent appendicectomy between January 2014 and July 2020 at 2 hospitals in the Illawarra Shoalhaven Local Health District, New South Wales, Australia, using the PM2 approach for surgical pathway identification and subsequent statistical analyses.

RESULTS

Among 3,943 patients, 3,606 (91.5%) followed an 11-step main pathway: (1) emergency department admission, (2) surgery booking, (3) anesthesia start, (4) operating room entry, (5) surgery start, (6) surgery end, (7) anesthesia end, (8) operating room discharge, (9) postanesthesia care unit admission, (10) postanesthesia care unit discharge, and (11) hospital discharge. The median length of stay was 48.13 hours (interquartile range 32.74). The main pathway differed from either variation 1 (n = 246, 6.2%) or variation 2 (n = 30, 0.8%) only in the timing and location of anesthesia administration or conclusion. Variation 3 (n = 26, 0.7%) included patients who underwent appendicectomy twice, whereas variation 4 (n = 25, 0.6%) included patients booked for surgery before emergency department admission through community doctor referrals. Thirteen exceptional cases experienced combinations of the aforementioned pathways. The length of stay and phase durations varied between the main pathway and these variations.

CONCLUSION

The appendicectomy pathway was largely standardized across the studied hospitals, with the location of anesthesia administration or conclusion affecting specific stages but not the overall length of stay. Only a complex 2-surgery pathway increased length of stay.

CvAMoS—Event Abstraction Using Contextual Information

Process mining analyzes events that are logged during the execution of a process, with the aim of gathering useful information and knowledge. Process discovery algorithms derive process models that represent these processes. The level of abstraction at which the process model is represented is reflected in the granularity of the event log. When a process is captured by the usage of sensor systems, process activities are recorded at the sensor-level in the form of sensor readings, and are therefore too fine-grained and non-explanatory. To increase the understandability of the process model, events need to be abstracted into higher-level activities that provide a more meaningful representation of the process. The abstraction becomes more relevant and challenging when the process involves human behavior, as the flexible nature of human actions can make it harder to identify and abstract meaningful activities. This paper proposes CvAMoS, a trace-based approach for event abstraction, which focuses on identifying motifs while taking context into account. A motif is a recurring sequence of events that represents an activity that took place under specific circumstances depicted by the context. Context information is logged in the event log in the form of environmental sensor readings (e.g., the temperature and light sensors). The presented algorithm uses a distance function to deal with the variability in the execution of activities. The result is a set of meaningful and interpretable motifs. The algorithm has been tested on both synthetic and real datasets, and compared to the state of the art. CvAMoS is implemented as a Java application and the code is freely available.

Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress

Introduction

Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities.

Methods

Following a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients' progress over time.

Results

This work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients' care processes in the context of the LifeChamps project together with a graphical and navigable Process Indicator in the context of prostate cancer patients.

Discussion

The tool presented includes Process Mining techniques to infer actual processes and present understandable results visually and navigable, looking for different types of patients, trajectories, and behaviors.

Characterization of Laboratory Flow and Performance for Process Improvements via Application of Process Mining

BACKGROUND

The rising level of laboratory automation provides an increasing number of logged events that can be used for the characterization of laboratory performance and process improvements. This abundance of data is often underutilized for improving laboratory efficiency.

OBJECTIVES

The first aim of this descriptive study is to provide a structured approach for transforming raw laboratory data to data that is suitable for process mining. The second aim is to describe a process mining approach for mapping and characterizing the sample flow in a clinical chemistry laboratory to identify areas for improvement in the testing process.

METHODS

Data were extracted from instrument log files and the middleware between laboratory instruments and information technology infrastructure. Process mining was used for automated process discovery and analysis. Laboratory performance was quantified in terms of relevant key performance indicators (KPIs): turnaround time, timeliness, workload, work-in-process, and machine downtime.

RESULTS

The method was applied to two Dutch university hospital clinical chemistry laboratories. We identified areas where alternative routes might increase laboratory efficiency and observed the negative effects of machine downtime on laboratory performance. This encourages the laboratory to review sample routes in its analyzer lines, the routes of high priority samples during instrument downtime, as well as the preventive maintenance policy.

CONCLUSION

This article provides the first application of process mining to event data from a medical diagnostic laboratory for automated process model discovery. Our study shows that process mining, with the use of relevant KPIs, provides valuable insights for laboratories that motivates the disclosure and increased utilization of laboratory event data, which in turn drive the analytical staff to intervene in the process to achieve the set performance goals. Our approach is vendor independent and widely applicable for all medical diagnostic laboratories.

An ambient assisted living architecture for hospital at home coupled with a process-oriented perspective

The growing number of next-generation applications offers a relevant opportunity for healthcare services, generating an urgent need for architectures for systems integration. Moreover, the huge amount of stored information related to events can be explored by adopting a process-oriented perspective. This paper discusses an Ambient Assisted Living healthcare architecture to manage hospital home-care services. The proposed solution relies on adopting an event manager to integrate sources ranging from personal devices to web-based applications. Data are processed on a federated cloud platform offering computing infrastructure and storage resources to improve scientific research. In a second step, a business process analysis of telehealth and telemedicine applications is considered. An initial study explored the business process flow to capture the main sequences of tasks, activities, events. This step paves the way for the integration of process mining techniques to compliance monitoring in an AAL architecture framework.

Process mining based on patient waiting time: an application in health processes

Purpose

Similar to many business processes, waiting times are also essential for health care processes, especially in obstetrics and gynecology outpatient department (GOD), because pregnant women may be affected by long waiting times. Since creating process models manually presents subjective and nonrealistic flows, this study aims to meet the need of an objective and realistic method.

Design/methodology/approach

In this study, the authors investigate time-related bottlenecks in both departments for different doctors by process mining. Process mining is a pragmatic analysis to obtain meaningful insights through event logs. It applies data mining techniques to business process management with more comprehensive perspectives. Process mining in this study enables to automatically create patient flows to compare considering each department and doctor.

Findings

The study concludes that average waiting times in the GOD are higher than obstetrics outpatient department. However, waiting times in departments can change inversely for different doctors.

Research limitations/implications

The event log was created by expert opinions because activities in the processes had just starting timestamp. The ending time of activity was computed by considering the average duration of the corresponding activity under a normal distribution.

Originality/value

This study focuses on administrative (nonclinical) health processes in obstetrics and GOD. It uses a parallel activity log inference algorithm (PALIA) to produce process trees by handling duplicate activities. Infrequent information in health processes can have critical information about the patient. PALIA considers infrequent activities in the event log to extract meaningful information, in contrast to many discovery algorithms.

Elderly Perception on the Internet of Things-Based Integrated Smart-Home System

An integrated smart home system (ISHS) is an effective way to improve the quality of life of the elderly. The elderly’s willingness is essential to adopt an ISHS; to the best of our knowledge, no study has investigated the elderly’s perception of ISHS. Consequently, this study aims to investigate the elderly’s perception of the ISHS by comprehensively evaluating its possible benefits and negative responses. A set of sensors required for an ISHS was determined, and interviews were designed based on four factors: perceived comfort, perceived usability, perceived privacy, and perceived benefit. Subsequently, technological trials of the sensor-set followed by two focus group interviews were conducted on nine independently living elderly participants at a senior welfare center in South Korea. Consistent with previous studies, the results of this investigation indicate that elderly participants elicited negative responses regarding usability complexity, and discomfort to daily activities. Despite such negative responses, after acquiring enough awareness about the ISHS’s benefits, the elderly acknowledged its necessity and showed a high level of willingness. Furthermore, these results indicate that for a better adoption of an ISHS, sufficient awareness regarding its benefits and development of elderly-friendly smart home sensors that minimize negative responses are required.

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients' dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients' unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?

CAPHAR: context-aware personalized human activity recognition using associative learning in smart environments

The existing action recognition systems mainly focus on generalized methods to categorize human actions. However, the generalized systems cannot attain the same level of recognition performance for new users mainly due to the high variance in terms of human behavior and the way of performing actions, i.e. activity handling. The use of personalized models based on similarity was introduced to overcome the activity handling problem, but the improvement was found to be limited as the similarity was based on physiognomies rather than the behavior. Moreover, human interaction with contextual information has not been studied extensively in the domain of action recognition. Such interactions can provide an edge for both recognizing high-level activities and improving the personalization effect. In this paper, we propose the context-aware personalized human activity recognition (CAPHAR) framework which computes the class association rules between low-level actions/sensor activations and the contextual information to recognize high-level activities. The personalization in CAPHAR leverages the individual behavior process using a similarity metric to reduce the effect of the activity handling problem. The experimental results on the “daily lifelog” dataset show that CAPHAR can achieve at most 23.73% better accuracy for new users in comparison to the existing classification methods.

The Role of Bedroom Privacy in Social Interaction among Elderly Residents in Nursing Homes: An Exploratory Case Study of Hong Kong

Privacy is often overlooked in Hong Kong nursing homes with the majority of elderly residents living in shared bedrooms of three to five people. Only a few studies have used Bluetooth low energy indoor positioning systems to explore the relationship between privacy and social interaction among elderly residents. The study investigates the social behavioural patterns of elderly residents living in three-bed, four-bed, and five-bed rooms in a nursing home. Location data of 50 residents were used for the identification of mobility and social interaction patterns in relation to different degrees of privacy and tested for statistical significance. Privacy is found to have a weak negative correlation with mobility patterns and social behaviour, implying that the more privacy there is, the less mobility and more formal interaction is found. Residents who had more privacy did not spend more time in social space. Residents living in bedrooms that opened directly onto social space had higher social withdrawal tendencies, indicating the importance of transitional spaces between private and public areas. Friends’ rooms were used extensively by residents who had little privacy, however, the concept of friends’ rooms have rarely been discussed in nursing homes. There is evidence supporting the importance of privacy for social interaction. Future study directions include considering how other design factors, such as configuration and social space diversity, work with privacy to influence social interaction.

Individual Behavior Modeling with Sensors Using Process Mining

Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days.

Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application

The study presents some results of customer paths' analysis in a shopping mall. Bluetooth-based technology is used to collect data. The event log containing spatiotemporal information is analyzed with process mining. Process mining is a technique that enables one to see the whole process contrary to data-centric methods. The use of process mining can provide a readily-understandable view of the customer paths. We installed iBeacon devices, a Bluetooth-based positioning system, in the shopping mall. During December 2017 and January and February 2018, close to 8000 customer data were captured. We aim to investigate customer behaviors regarding gender by using their paths. We can determine the gender of customers if they go to the men's bathroom or women's bathroom. Since the study has a comprehensive scope, we focused on male and female customers' behaviors. This study shows that male and female customers have different behaviors. Their duration and paths, in general, are not similar. In addition, the study shows that the process mining technique is a viable way to analyze customer behavior using Bluetooth-based technology.

In-store behavioral analytics technology selection using fuzzy decision making

Purpose

With the emerging technologies, collecting and processing data about the behaviors of customers or employees in a specific location has become possible. The purpose of this paper is to evaluate existing data collection technologies.

Design/methodology/approach

Technology evaluation problem is handled as a multi-criteria decision-making (MCDM) problem. In this manner, a decision model containing four criteria and eight sub-criteria and four alternatives are formed. The problem is solved using hesitant analytic hierarchy process (AHP) and trapezoidal fuzzy numbers (TrFN).

Findings

The results show that the most important sub-criteria are: accuracy, quantity, ıntrospective and cost. Decision makers’ evaluate for alternatives, namely wireless fidelity (WiFi), camera, radio-frequency identification and Bluetooth. The best alternative is found as Bluetooth which is followed by WiFi and Camera.

Research limitations/implications

Technology evaluation problem, just like many other MCDM problems are solved using expert evaluations. Thus, the generalizability of the findings is low.

Originality/value

In this paper, technology selection problem has been handled using hesitant AHP for the first time. In addition, the original methodology is extended by using TrFN to represent the expert evaluations in a better way.

Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining

Background

Public health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes.

Objective

Health care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients.

Methods

This paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise.

Results

The method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner.

Conclusions

By discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.

An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

The urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an indoor positioning system for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.

Diabetes care related process modelling using Process Mining techniques. Lessons learned in the application of Interactive Pattern Recognition: coping with the Spaghetti Effect

Diabetes is one of the metabolic disorders with more growth expectations in next decades. The literature points to a correct self-management, to an appropriate treatment and to an adequate healthy lifestyle as a way to dramatically improve the quality of life of patients with diabetes. The implementation of a holistic diabetes care system, using rising information technologies for deploying cares based on the thesis of the Evidence-Based Medicine can be a effective solution to provide an adequate and continuous care to patients. However, the design and deployment of computer readable careflows is not a easy task. In this paper, we propose the use of Interactive Pattern Recognition techniques for the iterative design of those protocols and we analyze the problems of using Process Mining to infer careflows and how to how to cope with the resulting Spaghetti Effect.

Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems

The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.

Using process mining for automatic support of clinical pathways design

The creation of tools supporting the automatization of the standardization and continuous control of healthcare processes can become a significant helping tool for clinical experts and healthcare systems willing to reduce variability in clinical practice. The reduction in the complexity of design and deployment of standard Clinical Pathways can enhance the possibilities for effective usage of computer assisted guidance systems for professionals and assure the quality of the provided care. Several technologies have been used in the past for trying to support these activities but they have not been able to generate the disruptive change required to foster the general adoption of standardization in this domain due to the high volume of work, resources, and knowledge required to adequately create practical protocols that can be used in practice. This chapter proposes the use of the PALIA algorithm, based in Activity-Based process mining techniques, as a new technology to infer the actual processes from the real execution logs to be used in the design and quality control of healthcare processes.

Context graphs as an efficient and user-friendly method of describing and recognizing a situation in AAL

In the field of ambient assisted living, the best results are achieved with systems that are less intrusive and more intelligent, that can easily integrate both formal and informal caregivers and that can easily adapt to the changes in the situation of the elderly or disabled person. This paper presents a graph-based representation for context information and a simple and intuitive method for situation recognition. Both the input and the results are easy to visualize, understand and use. Experiments have been performed on several AAL-specific scenarios.